The article discusses how to create a pattern classifier using probability theory. The goal is to design a classifier that makes the fewest mistakes. This classifier, called the minimum error rate classifier, is based on Bayes theory and works well with a uniform cost function. It can also be improved by adding a reject option. The focus is on a two-class classification problem, known as 'detection', where the Bayes decision rule can be simplified.